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A Bayesian Approach to Estimating the Long Memory Parameter
A Bayesian Approach to Estimating the Long Memory Parameter
RRS2007-13
Abstract
We develop a Bayesian procedure for analyzing stationary long-range dependent processes. Specifically, we consider the fractional exponential model (FEXP) to estimate the memory parameter of a stationary long-memory Gaussian time series. Further, the method we propose is hierarchical and integrates over all possible models, thus reducing underestimation of uncertainty at the model-selection stage. Additionally, we establish Bayesian consistency of the memory parameter under mild conditions on the data process. Finally the suggested procedure is investigated on simulated and real data.
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Page Last Revised - October 28, 2021
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